# LangGraph Reflection Research Agent: Iterative LLM Research and Refinement ## Contents - [1. Purpose of Project](#1-purpose-of-project) - [2. Input and Output](#2-input-and-output) - [3. LLM Technology Stack](#3-llm-technology-stack) - [4. Challenges and Difficulties](#4-challenges-and-difficulties) - [5. Future Business Impact and Further Improvements](#5-future-business-impact-and-further-improvements) - [6. Target Audience and Benefits](#6-target-audience-and-benefits) - [7. Advantages, Disadvantages and Tradeoffs](#7-advantages-disadvantages-and-tradeoffs) - [8. Setup](#8-setup) - [9. Code Explanation](#9-code-explanation) * [`main.py`](#mainpy) * [`chains.py`](#chainspy) * [`tool_executor.py`](#tool_executorpy) * [`schemas.py`](#schemaspy) - [10. How it Works](#10-how-it-works) * [Class/Function Breakdown](#classfunction-breakdown) - [11. Crucial Functions](#11-crucial-functions) - [12. Future Improvements](#12-future-improvements) - [13. Summary](#13-summary) * [LangGraph Researcher Agent](#langgraph-researcher-agent) * [Reason for LangGraph in Multi-Agent Architecture](#reason-for-langgraph-in-multi-agent-architecture) * [GPT Researcher Example](#gpt-researcher-example) - [14. Debugging Notes](#14-debugging-notes) - [Acknowledgements](#acknowledgements) ## 1. Purpose of Project The LangGraph project implements a "Reflexion Research Agent" designed to iteratively refine answers to user queries using a Large Language Model (LLM) and web search. It simulates a research process where an initial answer is generated, critiqued, and revised based on information gathered from web searches, all managed by a LangGraph workflow. The project leverages LangChain, OpenAI's GPT-4, and the Tavily Search API to automate research and improve the quality of generated content. It highlights the use of LangGraph for complex, multi-step LLM applications and addresses challenges related to dependency management, LLM output parsing, and tool integration. Abstract: Project implements a "Reflexion Research Agent" that iteratively refines answers to user queries using a Large Language Model (LLM) and web search. It simulates a research process where an initial answer is generated, critiqued, and then revised based on additional information gathered from web searches. The agent uses LangGraph to manage the state and flow of the process. ## 2. Input and Output **Input:** * A user's question or query in natural language (e.g., "Write about DeepSeek MoE and GRPO, list its impact and applications to future AI research."). ```Python Write about DeepSeek MoE and GRPO, list its impact and applications to future AI research. ``` **Output:** * A refined answer to the user's query, including: * A detailed answer (approximately 250 words). * A reflection/critique of the answer. * References to supporting information.

[//]: #![image](https://github.com/user-attachments/assets/b08e55f0-846c-4bf1-aac3-83b78a1866bf) [//]: #![image](https://github.com/user-attachments/assets/1036670d-78f3-4f6f-a812-a46f99b0072c) ```python /Users/junfanzhu/Desktop/reflexion-agent/venv/bin/python3.10 /Users/junfanzhu/Desktop/reflexion-agent/main.py +-----------+ | __start__ | +-----------+ * * * +-------+ | draft | +-------+ *** ... * . ** ... +---------------+ . | execute_tools | ... +---------------+ . *** ... * . ** .. +--------+ | revise | +--------+ . . . +---------+ | __end__ | +---------+ %%{init: {'flowchart': {'curve': 'linear'}}}%% graph TD; __start__([

__start__

]):::first draft(draft) execute_tools(execute_tools) revise(revise) __end__([

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]):::last __start__ --> draft; draft --> execute_tools; execute_tools --> revise; revise -.-> draft; revise -.-> execute_tools; revise -.-> __end__; classDef default fill:#f2f0ff,line-height:1.2 classDef first fill-opacity:0 classDef last fill:#bfb6fc Hello Reflexion Agent! DeepSeek MoE (Mixture of Experts) and GRPO (Group Relative Policy Optimization) are groundbreaking advancements in AI research with profound implications for the future of AI technologies. **DeepSeek MoE** is a novel language model architecture integrating MoE with a Multi-Head Latent Attention (MLA) mechanism and RMSNorm, aimed at improving scalability and inference efficiency. This architecture leads to a 40% reduction in computational costs while maintaining top-notch accuracy. DeepSeek MoE, released as DeepSeek V3, further enhances MoE load balancing and multi-token prediction training, marking a leap in the development of open-source large language models (LLMs). Its open-source framework promotes the deployment of advanced AI tools in software development, business operations, and education, providing a cost-efficient yet high-performance solution [1][2][3]. **GRPO**, on the other hand, is a reinforcement learning algorithm specifically designed for training LLMs in complex tasks like solving math problems or writing code. It introduces a novel approach by eliminating the need for explicit reward modeling, instead using a preference-based approach to optimize the model. This results in improved alignment with human feedback and significantly advances reinforcement learning methods tailored for mathematical reasoning and beyond [4][5][6]. Together, DeepSeek MoE and GRPO signify a step towards making AI models more efficient, accessible, and capable of generating high-quality responses, supporting a wide range of applications across various fields. 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However, based on the terms used, we can infer possible implications and applications in AI research that similar technologies could have.\\n\\nMixture of Experts (MoE) models are a type of ensemble learning technique where multiple expert models contribute to solving parts of a problem, with a gating mechanism to decide which expert to use for each task. MoE models can significantly enhance performance in complex tasks by leveraging the specialized knowledge of each expert. If \'DeepSeek MoE\' refers to an advanced implementation or application of MoE, its impact on AI could be substantial, offering more efficient, scalable, and precise solutions to intricate problems across domains such as natural language processing, computer vision, and robotics.\\n\\n\'GRPO\' might indicate a novel optimization or policy learning approach in the context of reinforcement learning or another area. 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Given the dynamic nature of the AI field, the exact terms 'DeepSeek MoE' (Mixture of Experts) and 'GRPO' might refer to novel concepts, technologies, or methodologies introduced after my last update. However, based on the terms used, we can infer possible implications and applications in AI research that similar technologies could have.\n\nMixture of Experts (MoE) models are a type of ensemble learning technique where multiple expert models contribute to solving parts of a problem, with a gating mechanism to decide which expert to use for each task. MoE models can significantly enhance performance in complex tasks by leveraging the specialized knowledge of each expert. 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If related to optimizing the performance of AI models or decision-making processes, such a methodology could improve learning efficiency, adaptability, and effectiveness in AI applications ranging from autonomous systems to personalized AI services.\n\nBoth these technologies could push the boundaries of AI research, fostering innovations that make AI systems more capable, resource-efficient, and tailored to complex tasks and environments.", 'reflection': {'missing': 'The answer does not specify any concrete details about DeepSeek MoE and GRPO owing to their apparent non-existence within the currently available knowledge. It lacks specific information about their origins, creators, technical mechanisms, or documented use cases.', 'superfluous': "The theoretical discussion about Mixture of Experts (MoE) models and the speculation about 'GRPO' might be considered unnecessary in light of the question's presumed focus on actual, identifiable technologies or research findings related to 'DeepSeek MoE' and 'GRPO'."}, 'search_queries': ['DeepSeek MoE technology', 'GRPO methodology in AI', 'Latest AI research breakthroughs 2024']}, 'id': 'call_WqNVubAo7XCGZkRn0197rkqt', 'type': 'tool_call'}], usage_metadata={'input_tokens': 211, 'output_tokens': 452, 'total_tokens': 663, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}), ToolMessage(content='[[{"url": "https://joelotepawembo.medium.com/deepseekmoe-bridging-efficiency-and-capacity-in-large-language-models-using-deepseek-model-from-dbd4e852a637", "content": "DeepSeekMoE: Bridging Efficiency and Capacity in Large Language Models using DeepSeek Model from China | by Joel Wembo | Jan, 2025 | Medium The DeepSeekMoE model, as outlined in the provided architecture, represents a significant advancement in the design of large-scale language models by integrating Mixture of Experts (MoE) with novel attention mechanisms and normalization strategies. DeepSeekMoE is a novel language model architecture that integrates Mixture of Experts (MoE) with a Multi-Head Latent Attention (MLA) mechanism and RMSNorm to achieve unprecedented scalability and inference efficiency. By introducing shared experts, dynamic routing, and latent variable caching, DeepSeekMoE reduces computational costs by up to 40% compared to traditional MoE models while maintaining state-of-the-art accuracy."}, {"url": "https://medium.com/yugen-ai-technology-blog/deepseek-v3-advances-in-moe-load-balancing-and-multi-token-prediction-training-f6d68c59749c", "content": "DeepSeek-V3 — Advances in MoE Load Balancing and Multi-Token Prediction Training | by Yugen.ai | Yugen.ai Technology Blog | Jan, 2025 | Medium DeepSeek-V3 — Advances in MoE Load Balancing and Multi-Token Prediction Training 2024 has been a great year for Open Source LLMs. Towards the end of 2024, DeepSeek released DeepSeek-V3, a 671B parameter MOE (mixture-of-experts) language model. The MOE (Mixture-of-Experts) architecture uses multiple expert networks for prediction. While MoEs have gained popularity in language model architectures in the last few years, its foundations can be traced back to the Learning Factored Representations in a Deep Mixture of Experts paper. The DeepSeek-V3 paper builds on the MTP work proposed in the Better & Faster Large Language Models via Multi-token Prediction paper."}, {"url": "https://daily.dev/blog/deepseek-everything-you-need-to-know-about-this-new-llm-in-one-place", "content": "[DeepSeek](https://www.deepseek.com/) LLM ExplainedKey Features of DeepSeekMixture-of-Experts (MoE) SystemMulti-Head Latent Attention (MLA)Handling Long ContextsComparing DeepSeek to Other LLMsPerformance MetricsOpen-Source FrameworkUsing DeepSeek in Different FieldsFor Software DevelopmentFor Business OperationsFor EducationHow to Start Using DeepSeekSteps to Begin with DeepSeekTips for IntegrationConclusion: DeepSeek\'s Role in AISummary of Features and UsesFuture PossibilitiesRelated Blog Posts Explore a groundbreaking AI model that combines efficiency, top performance, and open-source accessibility for software development and automation. DeepSeek is a cutting-edge large language model (LLM) built to tackle software development, natural language processing, and business automation. This approach makes DeepSeek a practical option for developers who want to balance cost-efficiency with high performance. With its task-specific parameter activation and open-source framework, DeepSeek has made advanced AI tools more accessible to businesses of different sizes [2]. DeepSeek\'s open-source approach and efficient design are changing how AI is developed and used."}, {"url": "https://github.com/deepseek-ai/DeepSeek-MoE", "content": "GitHub - deepseek-ai/DeepSeek-MoE: DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models DeepSeekMoE 16B is a Mixture-of-Experts (MoE) language model with 16.4B parameters. For research purposes, we release the model checkpoints of DeepSeekMoE 16B Base and DeepSeekMoE 16B Chat to the public, which can be deployed on a single GPU with 40GB of memory without the need for quantization. We evaluate DeepSeekMoE 16B on various benchmarks and compare it with a series of models, as shown in the following. DeepSeek 7B is a dense model trained on the same corpus as DeepSeekMoE 16B. We release the DeepSeekMoE 16B, including both base and chat models, to the public. model_name = \\"deepseek-ai/deepseek-moe-16b-chat\\" After data preparation, you can use the sample shell script to finetune the DeepSeekMoE model. The use of DeepSeekMoE models is subject to the Model License."}, {"url": "https://arxiv.org/abs/2401.06066", "content": "arXiv:2401.06066 In the era of large language models, Mixture-of-Experts (MoE) is a promising architecture for managing computational costs when scaling up model parameters. Starting from a modest scale with 2B parameters, we demonstrate that DeepSeekMoE 2B achieves comparable performance with GShard 2.9B, which has 1.5 times the expert parameters and computation. Subsequently, we scale up DeepSeekMoE to 16B parameters and show that it achieves comparable performance with LLaMA2 7B, with only about 40% of computations. Further, our preliminary efforts to scale up DeepSeekMoE to 145B parameters consistently validate its substantial advantages over the GShard architecture, and show its performance comparable with DeepSeek 67B, using only 28.5% (maybe even 18.2%) of computations. Cite as: arXiv:2401.06066 [cs.CL] (or arXiv:2401.06066v1 [cs.CL] for this version) Bibliographic and Citation Tools Bibliographic Explorer Toggle Connected Papers Toggle"}], [{"url": "https://aiengineering.academy/LLM/TheoryBehindFinetuning/GRPO/", "content": "Group Relative Policy Optimization (GRPO) is a reinforcement learning algorithm designed to train large language models (LLMs) for complex tasks like solving math problems or writing code. The reward model, initially trained on a base model like DeepSeekMath-Base 7B with a learning rate of 2e-5, evaluates response quality based on accuracy and formatting, as noted in AWS | Community | Deep dive into Group Relative Policy Optimization (GRPO). Simplified Advantage Estimation: Using group means for baseline makes advantage calculation straightforward and efficient, reducing variance, as noted in The Math Behind DeepSeek: A Deep Dive into Group Relative Policy Optimization (GRPO) | by Sahin Ahmed, Data Scientist | Jan, 2025 | Medium."}, {"url": "https://www.marktechpost.com/2024/06/28/a-deep-dive-into-group-relative-policy-optimization-grpo-method-enhancing-mathematical-reasoning-in-open-language-models/", "content": "A Deep Dive into Group Relative Policy Optimization (GRPO) Method: Enhancing Mathematical Reasoning in Open Language Models - MarkTechPost Home Tech News AI Paper Summary A Deep Dive into Group Relative Policy Optimization (GRPO) Method: Enhancing Mathematical... A Deep Dive into Group Relative Policy Optimization (GRPO) Method: Enhancing Mathematical Reasoning in Open Language Models Group Relative Policy Optimization (GRPO) is a novel reinforcement learning method introduced in the DeepSeekMath paper earlier this year. Group Relative Policy Optimization (GRPO) significantly advances reinforcement learning methods tailored for mathematical reasoning. PRIME: An Open-Source Solution for Online Reinforcement Learning with Process Rewards to Advance Reasoning Abilities of Language Models Beyond Imitation or Distillation"}, {"url": "https://medium.com/data-science-in-your-pocket/what-is-grpo-the-rl-algorithm-used-to-train-deepseek-12acc19798d3", "content": "In Reinforcement Learning (RL), a policy is a strategy that an agent uses to decide which action to take in a given state. The value function helps improve the policy by estimating which states/actions lead to higher rewards. GRPO (Group Relative Policy Optimization) is a method used in reinforcement learning (RL) to help a model learn better by comparing different actions and making small, controlled updates using a group of observations. Trying Different Paths: The robot tries out a few different paths (actions) from its current strategy (policy). For a given state s, GRPO generates a group of N actions {a1,a2,…,aN} using the current policy πθ. Group Comparison: By comparing actions within a group, GRPO reduces the variance of policy updates and ensures more stable learning."}, {"url": "https://www.analyticsvidhya.com/blog/2025/02/llm-optimization/", "content": "Enter\xa0DeepSeek’s Group Relative Policy Optimization (GRPO)—a breakthrough that\xa0eliminated the need for explicit reward modeling\xa0by directly optimizing\xa0preference rankings. Learn the fundamentals of policy optimization, including PG, TRPO, and PPO.Explore DPO and GRPO for preference-based LLM training without explicit reward models. Unlike traditional policy optimization methods that operate on single preference pairs, GRPO leverages group-wise preference ranking, enabling better alignment with human feedback in large-scale LLM fine-tuning. Overall, the expected outcome of GRPO fine-tuning is a well-optimized model capable of generating high-quality responses ranked effectively based on preference learning. Why do LLMs need preference optimization techniques like DPO and GRPO?Ans. Traditional RL methods focus on maximizing numerical rewards, which do not always align with human expectations in language models."}, {"url": "https://medium.com/@sahin.samia/the-math-behind-deepseek-a-deep-dive-into-group-relative-policy-optimization-grpo-8a75007491ba", "content": "This blog dives into the math behind Group Relative Policy Optimization (GRPO), the core reinforcement learning algorithm that drives DeepSeek’s exceptional reasoning capabilities. Group Relative Policy Optimization (GRPO) is a reinforcement learning (RL) algorithm specifically designed to enhance reasoning capabilities in Large Language Models (LLMs). Unlike traditional RL methods, which rely heavily on external evaluators (critics) to guide learning, GRPO optimizes the model by evaluating groups of responses relative to one another. The objective function in Group Relative Policy Optimization (GRPO) defines how the model learns to improve its policy, driving its ability to generate high-quality responses. The GRPO (Group Relative Policy Optimization) objective function is like a recipe for teaching a model to get better at generating answers by comparing its own responses and improving step by step."}], [{"url": "https://blog.google/technology/ai/2024-ai-extraordinary-progress-advancement/", "content": "Google released Gemini 2.0, a powerful AI model designed for the \\"agentic era,\\" and integrated it into various products. We furthered our industry-leading research in AI safety, developing new tools and techniques and integrating these advances into our latest models. We expanded SynthID’s capabilities to watermarking AI-generated text in the Gemini app and web experience, and video in Veo. To help increase overall transparency online, not just with content created by Google gen AI tools, we also joined the Coalition for Content Provenance and Authenticity (C2PA) as a steering committee member and collaborated on a new, more secure version of the technical standard, Content Credentials."}, {"url": "https://www.topbots.com/ai-research-papers-2024/", "content": "In this article, we delve into ten groundbreaking research papers that expand the frontiers of AI across diverse domains, including large language models, multimodal processing, video generation and editing, and the creation of interactive environments. Produced by leading research labs such as Meta, Google DeepMind, Stability AI, Anthropic, and Microsoft, these studies showcase innovative approaches, including scaling down powerful models for efficient on-device use, extending multimodal reasoning across millions of tokens, and achieving unmatched fidelity in video and audio synthesis. Unified Multimodal Processing: The research introduces a seamless integration of text and visual inputs (e.g., images, charts, and videos), expanding the model’s ability to perform complex multimodal reasoning and analysis without requiring task-specific finetuning."}, {"url": "https://blog.google/technology/ai/google-ai-big-scientific-breakthroughs-2024/", "content": "How Google AI is advancing science AI is rapidly advancing science, with breakthroughs in fields like protein structure prediction, brain mapping, and flood forecasting. In 2024, Google Research expanded this coverage to 100 countries and 700 million people worldwide — and improved the AI model so it offers the same accuracy at a seven-day lead time as the previous model had at five. About Google"}, {"url": "https://www.forbes.com/sites/bernardmarr/2024/12/16/6-game-changing-ai-breakthroughs-that-defined-2024/", "content": "From Apple’s entry into generative AI to unprecedented achievements in robotics and art, 2024 marked ... Apple Enters The Generative AI Arena With Apple Intelligence By integrating OpenAI-powered generative language and graphics functionality across its product ecosystem, it created a typically refined, Apple-shaped gateway into the world of day-to-day AI for millions of non-techy people. Painting Created By AI Robot Sells For $1 Million The last couple of years have seen an explosion in AI art – but perhaps the most mind-blowing milestone was passed when Ai-Da became the first humanoid robot to sell a piece of artwork at auction. In order to do so, please follow the posting rules in our site\'s\xa0Terms of Service."}, {"url": "https://research.google/blog/google-research-2024-breakthroughs-for-impact-at-every-scale/", "content": "Never before were we able to advance technologies like AI, foundational ML, algorithms, and quantum computing with the pace, purpose and partnerships that we benefit from today, enabling us to drive innovation and address some of the greatest challenges and scientific questions of our times. In our research on learning from aggregate data, we introduced a novel approach for constructing aggregation datasets, and explored various algorithmic aspects of model learning from aggregated data, which achieved optimistic sample complexity rates in this setting. As computational techniques and ML models become progressively more advanced and accurate, our researchers are able to address challenges at scale, from GPS accuracy to the pressing issue of climate change."}]]', name='AnswerQuestion', id='211c67d5-9a9b-4d90-8d8e-e25712e40b6c', tool_call_id='call_WqNVubAo7XCGZkRn0197rkqt'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_ABgsQQ0kFsl2Ppe1NzULRK20', 'function': {'arguments': '{"answer":"DeepSeek MoE and GRPO have emerged as significant advancements in AI research, each contributing to the field in unique ways. DeepSeek MoE is a novel large language model (LLM) architecture that integrates Mixture of Experts (MoE) with Multi-Head Latent Attention (MLA) mechanisms and RMSNorm, offering unprecedented scalability and inference efficiency. This model reduces computational costs by up to 40% compared to traditional MoE models while maintaining state-of-the-art accuracy [1]. DeepSeek V3, another iteration, further advances MoE load balancing and multi-token prediction training [2], enhancing the performance of Open Source LLMs [3]. With its open-source framework, DeepSeek facilitates the development and use of advanced AI tools across various fields, including software development, business operations, and education [4].\\n\\nGRPO, or Group Relative Policy Optimization, is a reinforcement learning algorithm designed for LLM training in complex tasks like solving math problems or writing code. It eliminates the need for explicit reward modeling by employing a preference-based approach, optimizing preference rankings, thus aligning better with human feedback during the fine-tuning of large-scale LLMs [5]. This method represents a significant leap in reinforcement learning, focusing on enhancing the reasoning capabilities of LLMs [6]. 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DeepSeek MoE is a novel large language model (LLM) architecture that integrates Mixture of Experts (MoE) with Multi-Head Latent Attention (MLA) mechanisms and RMSNorm, offering unprecedented scalability and inference efficiency. This model reduces computational costs by up to 40% compared to traditional MoE models while maintaining state-of-the-art accuracy [1]. DeepSeek V3, another iteration, further advances MoE load balancing and multi-token prediction training [2], enhancing the performance of Open Source LLMs [3]. With its open-source framework, DeepSeek facilitates the development and use of advanced AI tools across various fields, including software development, business operations, and education [4].\n\nGRPO, or Group Relative Policy Optimization, is a reinforcement learning algorithm designed for LLM training in complex tasks like solving math problems or writing code. It eliminates the need for explicit reward modeling by employing a preference-based approach, optimizing preference rankings, thus aligning better with human feedback during the fine-tuning of large-scale LLMs [5]. This method represents a significant leap in reinforcement learning, focusing on enhancing the reasoning capabilities of LLMs [6]. Both DeepSeek MoE and GRPO exemplify the continuous evolution of AI technologies, each holding the potential to transform future AI applications and research by making models more efficient, accessible, and aligned with human preferences.', 'reflection': {'missing': 'The initial response overlooked specific details about DeepSeek MoE and GRPO, including their definitions, impacts, and applications in AI research. It lacked concrete information about how these technologies operate and how they contribute to advancements in AI.', 'superfluous': "The theoretical discussion of Mixture of Experts (MoE) models and speculative descriptions of GRPO's potential applications were superfluous, given that the actual technologies could have been described with concrete examples and applications.", 'search_queries': ['DeepSeek MoE applications', 'GRPO reinforcement learning', 'DeepSeek MoE efficiency improvements'], 'references': ['https://joelotepawembo.medium.com/deepseekmoe-bridging-efficiency-and-capacity-in-large-language-models-using-deepseek-model-from-dbd4e852a637', 'https://medium.com/yugen-ai-technology-blog/deepseek-v3-advances-in-moe-load-balancing-and-multi-token-prediction-training-f6d68c59749c', 'https://daily.dev/blog/deepseek-everything-you-need-to-know-about-this-new-llm-in-one-place', 'https://github.com/deepseek-ai/DeepSeek-MoE', 'https://aiengineering.academy/LLM/TheoryBehindFinetuning/GRPO/', 'https://www.marktechpost.com/2024/06/28/a-deep-dive-into-group-relative-policy-optimization-grpo-method-enhancing-mathematical-reasoning-in-open-language-models/']}}, 'id': 'call_ABgsQQ0kFsl2Ppe1NzULRK20', 'type': 'tool_call'}], usage_metadata={'input_tokens': 3840, 'output_tokens': 621, 'total_tokens': 4461, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}), ToolMessage(content="Error: 1 validation error for ReviseAnswer\nsearch_queries\n Field required [type=missing, input_value={'answer': 'DeepSeek MoE ...pen-language-models/']}}, input_type=dict]\n For further information visit https://errors.pydantic.dev/2.11/v/missing\n Please fix your mistakes.", name='ReviseAnswer', id='03dfae01-3f85-4419-9e0a-0b1066cc678c', tool_call_id='call_ABgsQQ0kFsl2Ppe1NzULRK20', status='error'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_IpycE26HBae1o1m6bOtrHmcD', 'function': {'arguments': '{"answer":"DeepSeek MoE and GRPO have emerged as significant advancements in AI research, each contributing to the field in unique ways. DeepSeek MoE is a novel large language model (LLM) architecture that integrates Mixture of Experts (MoE) with Multi-Head Latent Attention (MLA) mechanisms and RMSNorm, offering unprecedented scalability and inference efficiency. This model reduces computational costs by up to 40% compared to traditional MoE models while maintaining state-of-the-art accuracy [1]. DeepSeek V3, another iteration, further advances MoE load balancing and multi-token prediction training [2], enhancing the performance of Open Source LLMs [3]. With its open-source framework, DeepSeek facilitates the development and use of advanced AI tools across various fields, including software development, business operations, and education [4].\\n\\nGRPO, or Group Relative Policy Optimization, is a reinforcement learning algorithm designed for LLM training in complex tasks like solving math problems or writing code. It eliminates the need for explicit reward modeling by employing a preference-based approach, optimizing preference rankings, thus aligning better with human feedback during the fine-tuning of large-scale LLMs [5]. This method represents a significant leap in reinforcement learning, focusing on enhancing the reasoning capabilities of LLMs [6]. Both DeepSeek MoE and GRPO exemplify the continuous evolution of AI technologies, each holding the potential to transform future AI applications and research by making models more efficient, accessible, and aligned with human preferences.","reflection":{"missing":"The initial response overlooked specific details about DeepSeek MoE and GRPO, including their definitions, impacts, and applications in AI research. 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DeepSeek MoE is a novel large language model (LLM) architecture that integrates Mixture of Experts (MoE) with Multi-Head Latent Attention (MLA) mechanisms and RMSNorm, offering unprecedented scalability and inference efficiency. This model reduces computational costs by up to 40% compared to traditional MoE models while maintaining state-of-the-art accuracy [1]. DeepSeek V3, another iteration, further advances MoE load balancing and multi-token prediction training [2], enhancing the performance of Open Source LLMs [3]. With its open-source framework, DeepSeek facilitates the development and use of advanced AI tools across various fields, including software development, business operations, and education [4].\n\nGRPO, or Group Relative Policy Optimization, is a reinforcement learning algorithm designed for LLM training in complex tasks like solving math problems or writing code. It eliminates the need for explicit reward modeling by employing a preference-based approach, optimizing preference rankings, thus aligning better with human feedback during the fine-tuning of large-scale LLMs [5]. This method represents a significant leap in reinforcement learning, focusing on enhancing the reasoning capabilities of LLMs [6]. Both DeepSeek MoE and GRPO exemplify the continuous evolution of AI technologies, each holding the potential to transform future AI applications and research by making models more efficient, accessible, and aligned with human preferences.', 'reflection': {'missing': 'The initial response overlooked specific details about DeepSeek MoE and GRPO, including their definitions, impacts, and applications in AI research. It lacked concrete information about how these technologies operate and how they contribute to advancements in AI.', 'superfluous': "The theoretical discussion of Mixture of Experts (MoE) models and speculative descriptions of GRPO's potential applications were superfluous, given that the actual technologies could have been described with concrete examples and applications.", 'search_queries': ['DeepSeek MoE applications', 'GRPO reinforcement learning', 'DeepSeek MoE efficiency improvements'], 'references': ['https://joelotepawembo.medium.com/deepseekmoe-bridging-efficiency-and-capacity-in-large-language-models-using-deepseek-model-from-dbd4e852a637', 'https://medium.com/yugen-ai-technology-blog/deepseek-v3-advances-in-moe-load-balancing-and-multi-token-prediction-training-f6d68c59749c', 'https://daily.dev/blog/deepseek-everything-you-need-to-know-about-this-new-llm-in-one-place', 'https://github.com/deepseek-ai/DeepSeek-MoE', 'https://aiengineering.academy/LLM/TheoryBehindFinetuning/GRPO/', 'https://www.marktechpost.com/2024/06/28/a-deep-dive-into-group-relative-policy-optimization-grpo-method-enhancing-mathematical-reasoning-in-open-language-models/']}, 'search_queries': ['DeepSeek MoE applications', 'GRPO reinforcement learning', 'DeepSeek MoE efficiency improvements']}, 'id': 'call_IpycE26HBae1o1m6bOtrHmcD', 'type': 'tool_call'}], usage_metadata={'input_tokens': 4549, 'output_tokens': 647, 'total_tokens': 5196, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}), ToolMessage(content='[[{"url": "https://joelotepawembo.medium.com/deepseekmoe-bridging-efficiency-and-capacity-in-large-language-models-using-deepseek-model-from-dbd4e852a637", "content": "DeepSeekMoE is a novel language model architecture that integrates Mixture of Experts (MoE) with a Multi-Head Latent Attention (MLA) mechanism and RMSNorm."}, {"url": "https://medium.com/@joycebirkins/deepseek-4-official-papers-overview-deepseek-moe-mla-mtp-distillation-49a97b3b90a8", "content": "Deepseek V3 adopts an Auxiliary-Loss-Free Strategy for Load Balancing with dynamic routing and adaptive capacity, solving the load balancing problem in MoE, making it easier to train and fine-tune the model. Using the DeepSeek-R1 model to generate reasoning data for math, code, and logic puzzles, and training a specific domain expert model using supervised fine-tuning combined with reinforcement learning to handle the long-format content issue. This allows the reward model to inherit the knowledge and language understanding that DeepSeek-V3 learned during the supervised fine-tuning phase, thus better adapting to the needs of subsequent reinforcement learning tasks and reasonably evaluating the model’s outputs. Distillation involves using high-quality training data generated by the MoE large model (Deepseek V1), which includes CoTs, multi-step reasoning, answer distributions, etc."}, {"url": "https://daily.dev/blog/deepseek-everything-you-need-to-know-about-this-new-llm-in-one-place", "content": "[DeepSeek](https://www.deepseek.com/) LLM ExplainedKey Features of DeepSeekMixture-of-Experts (MoE) SystemMulti-Head Latent Attention (MLA)Handling Long ContextsComparing DeepSeek to Other LLMsPerformance MetricsOpen-Source FrameworkUsing DeepSeek in Different FieldsFor Software DevelopmentFor Business OperationsFor EducationHow to Start Using DeepSeekSteps to Begin with DeepSeekTips for IntegrationConclusion: DeepSeek\'s Role in AISummary of Features and UsesFuture PossibilitiesRelated Blog Posts Explore a groundbreaking AI model that combines efficiency, top performance, and open-source accessibility for software development and automation. DeepSeek is a cutting-edge large language model (LLM) built to tackle software development, natural language processing, and business automation. This approach makes DeepSeek a practical option for developers who want to balance cost-efficiency with high performance. With its task-specific parameter activation and open-source framework, DeepSeek has made advanced AI tools more accessible to businesses of different sizes [2]. DeepSeek\'s open-source approach and efficient design are changing how AI is developed and used."}, {"url": "https://tldv.io/blog/what-is-deepseek/", "content": "DeepSeek, like other AI models, collects and processes user inputs, but its data policies and geographic ties bring additional considerations. AI models often retain some interactions to refine their responses, but the extent of DeepSeek’s data storage remains unclear compared to more widely documented AI platforms like OpenAI’s ChatGPT. While DeepSeek presents itself as a competitive AI model, users should be aware of data privacy concerns. To recap it all, DeepSeek is an emerging AI model that has generated interest for its efficiency, structured reasoning, and potential lower cost of development compared to competitors like ChatGPT and Claude. tl;dv’s AI meeting agents automate recording, transcription, summarization, and integration into tools like CRMs and productivity platforms."}, {"url": "https://github.com/deepseek-ai/DeepSeek-MoE", "content": "GitHub - deepseek-ai/DeepSeek-MoE: DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models DeepSeekMoE 16B is a Mixture-of-Experts (MoE) language model with 16.4B parameters. For research purposes, we release the model checkpoints of DeepSeekMoE 16B Base and DeepSeekMoE 16B Chat to the public, which can be deployed on a single GPU with 40GB of memory without the need for quantization. We evaluate DeepSeekMoE 16B on various benchmarks and compare it with a series of models, as shown in the following. DeepSeek 7B is a dense model trained on the same corpus as DeepSeekMoE 16B. We release the DeepSeekMoE 16B, including both base and chat models, to the public. model_name = \\"deepseek-ai/deepseek-moe-16b-chat\\" After data preparation, you can use the sample shell script to finetune the DeepSeekMoE model. The use of DeepSeekMoE models is subject to the Model License."}], [{"url": "https://medium.com/data-science-in-your-pocket/what-is-grpo-the-rl-algorithm-used-to-train-deepseek-12acc19798d3", "content": "In Reinforcement Learning (RL), a policy is a strategy that an agent uses to decide which action to take in a given state. The value function helps improve the policy by estimating which states/actions lead to higher rewards. GRPO (Group Relative Policy Optimization) is a method used in reinforcement learning (RL) to help a model learn better by comparing different actions and making small, controlled updates using a group of observations. Trying Different Paths: The robot tries out a few different paths (actions) from its current strategy (policy). For a given state s, GRPO generates a group of N actions {a1,a2,…,aN} using the current policy πθ. Group Comparison: By comparing actions within a group, GRPO reduces the variance of policy updates and ensures more stable learning."}, {"url": "https://medium.com/@sahin.samia/the-math-behind-deepseek-a-deep-dive-into-group-relative-policy-optimization-grpo-8a75007491ba", "content": "This blog dives into the math behind Group Relative Policy Optimization (GRPO), the core reinforcement learning algorithm that drives DeepSeek’s exceptional reasoning capabilities. Group Relative Policy Optimization (GRPO) is a reinforcement learning (RL) algorithm specifically designed to enhance reasoning capabilities in Large Language Models (LLMs). Unlike traditional RL methods, which rely heavily on external evaluators (critics) to guide learning, GRPO optimizes the model by evaluating groups of responses relative to one another. The objective function in Group Relative Policy Optimization (GRPO) defines how the model learns to improve its policy, driving its ability to generate high-quality responses. The GRPO (Group Relative Policy Optimization) objective function is like a recipe for teaching a model to get better at generating answers by comparing its own responses and improving step by step."}, {"url": "https://www.marktechpost.com/2024/06/28/a-deep-dive-into-group-relative-policy-optimization-grpo-method-enhancing-mathematical-reasoning-in-open-language-models/", "content": "A Deep Dive into Group Relative Policy Optimization (GRPO) Method: Enhancing Mathematical Reasoning in Open Language Models - MarkTechPost Home Tech News AI Paper Summary A Deep Dive into Group Relative Policy Optimization (GRPO) Method: Enhancing Mathematical... A Deep Dive into Group Relative Policy Optimization (GRPO) Method: Enhancing Mathematical Reasoning in Open Language Models Group Relative Policy Optimization (GRPO) is a novel reinforcement learning method introduced in the DeepSeekMath paper earlier this year. Group Relative Policy Optimization (GRPO) significantly advances reinforcement learning methods tailored for mathematical reasoning. PRIME: An Open-Source Solution for Online Reinforcement Learning with Process Rewards to Advance Reasoning Abilities of Language Models Beyond Imitation or Distillation"}, {"url": "https://community.aws/content/2rJrpj6m2eh591fjMcRZ3ushpB7/deep-dive-into-group-relative-policy-optimization-grpo?lang=en", "content": "AWS | Community | Deep dive into Group Relative Policy Optimization (GRPO) However, as models grow larger and tasks become more complex, PPO\'s limitations—such as memory overhead and computational cost—have prompted the development of more advanced methods like Group Relative Policy Optimization (GRPO). The final stage uses reinforcement learning to optimize the model\'s policy (π_θ) using the Proximal Policy Optimization (PPO) algorithm. Now, PPO is a policy gradient method that optimizes a policy π_ø by maximizing a surrogate objective function (in this case, the approximate human preference/reward function). The group relative way that GRPO leverages to calculate the advantages, aligns well with the comparative nature of rewards models, as reward models are typically trained on datasets of comparisons between outputs on the same question."}, {"url": "https://ghost.oxen.ai/why-grpo-is-important-and-how-it-works/", "content": "The process to go from DeepSeek’s base pre-trained language model to a reasoning model was laid out in detail in the DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning paper. The value and reward models are typically of a comparable parameter count to the LLM you are training. 🧠 GRPO VRAM Requirements For the GPU Poor | Oxen.ai Since the release of DeepSeek-R1, Group Relative Policy Optimization (GRPO) has become the talk of the town for Reinforcement Learning in Large Language Models due to its effectiveness and ease of training. In PPO, the value model’s original job is to try to estimate how good the tokens that are generated are, or how likely they are to give a high reward. You don’t even need a full reward model LLM in the loop during training."}], [{"url": "https://medium.com/@niejam/how-mixture-of-experts-moe-enhances-the-efficiency-of-deepseek-b0ae1b9a0092", "content": "MoE enhances the efficiency of DeepSeek by enabling sparse activation, task specialization, reduced computational costs, and improved training dynamics."}, {"url": "https://isitvritra101.medium.com/how-does-deepseeks-mixture-of-experts-architecture-improve-performance-08fcdab7e35a", "content": "DeepSeek\'s mixture-of-experts (MoE) architecture enhances performance through computational efficiency, task specialization, and scalable resource allocation."}, {"url": "https://www.chipstrat.com/p/deepseek-moe-and-v2", "content": "These constraints present significant challenges that impede the widespread adoption and utilization of LLMs. In order to tackle this problem, we introduce DeepSeek-V2, a strong open-source Mixture-of-Experts (MoE) language model, characterized by economical training and efficient inference through an innovative Transformer architecture. These constraints present significant challenges that impede the widespread adoption and utilization of LLMs. In order to tackle this problem, we introduce DeepSeek-V2, a strong open-source Mixture-of-Experts (MoE) language model, characterized by economical training and efficient inference through an innovative Transformer architecture. … DeepSeek-V2, a strong open-source Mixture-of-Experts (MoE) language model, characterized by economical training and efficient inference through an innovative Transformer architecture. DeepSeek V2\'s combination of MoE, MLA, and communication optimizations significantly improves training and inference efficiency compared to their dense 67B model:"}, {"url": "https://creativestrategies.com/deepseek-moe-v2/", "content": "Communication & Compute Optimizations – Implements load balancing and custom CUDA kernels to improve efficiency and reduce bottlenecks. Training"}, {"url": "https://dev.to/sayed_ali_alkamel/deepseek-and-the-power-of-mixture-of-experts-moe-ham", "content": "DeepSeek and the Power of Mixture of Experts (MoE) - DEV Community DeepSeek and the Power of Mixture of Experts (MoE) DeepSeek is causing a stir in the AI community with its open-source large language models (LLMs), and a key factor in its success is the Mixture of Experts (MoE) architecture. DeepSeek\'s innovative use of MoE has positioned it as a leading force in the world of open-source LLMs. By combining expert specialization with efficient resource utilization, DeepSeek achieves remarkable performance and scalability. DeepSeek, Mixture of Experts, MoE, Large Language Model, LLM, AI, Artificial Intelligence, Deep Learning, Natural Language Processing, NLP, Code Generation, Business Processes, Education, Open Source, Efficiency, Scalability, Performance, Training Costs, Inference Speed, DeepSeek-V3, DeepSeekMoE, Multi-Token Prediction, MTP"}]]', name='ReviseAnswer', id='b4b3b5a7-4d28-4848-b446-c4c26965e1a6', tool_call_id='call_IpycE26HBae1o1m6bOtrHmcD'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_w0FrbHY4x2FmYP0Xxktg6lk3', 'function': {'arguments': '{"answer":"DeepSeek MoE (Mixture of Experts) and GRPO (Group Relative Policy Optimization) are groundbreaking advancements in AI research with profound implications for the future of AI technologies.\\n\\n**DeepSeek MoE** is a novel language model architecture integrating MoE with a Multi-Head Latent Attention (MLA) mechanism and RMSNorm, aimed at improving scalability and inference efficiency. This architecture leads to a 40% reduction in computational costs while maintaining top-notch accuracy. DeepSeek MoE, released as DeepSeek V3, further enhances MoE load balancing and multi-token prediction training, marking a leap in the development of open-source large language models (LLMs). Its open-source framework promotes the deployment of advanced AI tools in software development, business operations, and education, providing a cost-efficient yet high-performance solution [1][2][3].\\n\\n**GRPO**, on the other hand, is a reinforcement learning algorithm specifically designed for training LLMs in complex tasks like solving math problems or writing code. It introduces a novel approach by eliminating the need for explicit reward modeling, instead using a preference-based approach to optimize the model. This results in improved alignment with human feedback and significantly advances reinforcement learning methods tailored for mathematical reasoning and beyond [4][5][6].\\n\\nTogether, DeepSeek MoE and GRPO signify a step towards making AI models more efficient, accessible, and capable of generating high-quality responses, supporting a wide range of applications across various fields.","references":["https://joelotepawembo.medium.com/deepseekmoe-bridging-efficiency-and-capacity-in-large-language-models-using-deepseek-model-from-dbd4e852a637","https://medium.com/@joycebirkins/deepseek-4-official-papers-overview-deepseek-moe-mla-mtp-distillation-49a97b3b90a8","https://daily.dev/blog/deepseek-everything-you-need-to-know-about-this-new-llm-in-one-place","https://medium.com/data-science-in-your-pocket/what-is-grpo-the-rl-algorithm-used-to-train-deepseek-12acc19798d3","https://medium.com/@sahin.samia/the-math-behind-deepseek-a-deep-dive-into-group-relative-policy-optimization-grpo-8a75007491ba","https://www.marktechpost.com/2024/06/28/a-deep-dive-into-group-relative-policy-optimization-grpo-method-enhancing-mathematical-reasoning-in-open-language-models/"]}', 'name': 'ReviseAnswer'}, 'type': 'function'}], 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 540, 'prompt_tokens': 7875, 'total_tokens': 8415, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4-0125-preview', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-ab52d0db-8cd6-4f58-9fc8-748511ab0f11-0', tool_calls=[{'name': 'ReviseAnswer', 'args': {'answer': 'DeepSeek MoE (Mixture of Experts) and GRPO (Group Relative Policy Optimization) are groundbreaking advancements in AI research with profound implications for the future of AI technologies.\n\n**DeepSeek MoE** is a novel language model architecture integrating MoE with a Multi-Head Latent Attention (MLA) mechanism and RMSNorm, aimed at improving scalability and inference efficiency. This architecture leads to a 40% reduction in computational costs while maintaining top-notch accuracy. DeepSeek MoE, released as DeepSeek V3, further enhances MoE load balancing and multi-token prediction training, marking a leap in the development of open-source large language models (LLMs). Its open-source framework promotes the deployment of advanced AI tools in software development, business operations, and education, providing a cost-efficient yet high-performance solution [1][2][3].\n\n**GRPO**, on the other hand, is a reinforcement learning algorithm specifically designed for training LLMs in complex tasks like solving math problems or writing code. It introduces a novel approach by eliminating the need for explicit reward modeling, instead using a preference-based approach to optimize the model. This results in improved alignment with human feedback and significantly advances reinforcement learning methods tailored for mathematical reasoning and beyond [4][5][6].\n\nTogether, DeepSeek MoE and GRPO signify a step towards making AI models more efficient, accessible, and capable of generating high-quality responses, supporting a wide range of applications across various fields.', 'references': ['https://joelotepawembo.medium.com/deepseekmoe-bridging-efficiency-and-capacity-in-large-language-models-using-deepseek-model-from-dbd4e852a637', 'https://medium.com/@joycebirkins/deepseek-4-official-papers-overview-deepseek-moe-mla-mtp-distillation-49a97b3b90a8', 'https://daily.dev/blog/deepseek-everything-you-need-to-know-about-this-new-llm-in-one-place', 'https://medium.com/data-science-in-your-pocket/what-is-grpo-the-rl-algorithm-used-to-train-deepseek-12acc19798d3', 'https://medium.com/@sahin.samia/the-math-behind-deepseek-a-deep-dive-into-group-relative-policy-optimization-grpo-8a75007491ba', 'https://www.marktechpost.com/2024/06/28/a-deep-dive-into-group-relative-policy-optimization-grpo-method-enhancing-mathematical-reasoning-in-open-language-models/']}, 'id': 'call_w0FrbHY4x2FmYP0Xxktg6lk3', 'type': 'tool_call'}], usage_metadata={'input_tokens': 7875, 'output_tokens': 540, 'total_tokens': 8415, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}})] Process finished with exit code 0 ``` **LangSmith Trace:** https://smith.langchain.com/public/6da74f50-44e5-4fb2-9cd7-cf29bfc69419/r ![image](https://github.com/user-attachments/assets/a1f45aa3-05ef-457c-93e8-afbe261f14d0) Input: `Write about DeepSeek MoE and GRPO, list its impact and applications to future AI research.` Output: - Human: `Write about DeepSeek MoE and GRPO, list its impact and applications to future AI research.` - AI: `AnswerQuestion call_WqNVubAo7XCGZkRn0197rkqt` ```python answer: |- DeepSeek MoE and GRPO are not specifically recognized frameworks or models within the broad and rapidly evolving domain of AI research as of my last update. Given the dynamic nature of the AI field, the exact terms 'DeepSeek MoE' (Mixture of Experts) and 'GRPO' might refer to novel concepts, technologies, or methodologies introduced after my last update. However, based on the terms used, we can infer possible implications and applications in AI research that similar technologies could have. Mixture of Experts (MoE) models are a type of ensemble learning technique where multiple expert models contribute to solving parts of a problem, with a gating mechanism to decide which expert to use for each task. MoE models can significantly enhance performance in complex tasks by leveraging the specialized knowledge of each expert. If 'DeepSeek MoE' refers to an advanced implementation or application of MoE, its impact on AI could be substantial, offering more efficient, scalable, and precise solutions to intricate problems across domains such as natural language processing, computer vision, and robotics. 'GRPO' might indicate a novel optimization or policy learning approach in the context of reinforcement learning or another area. If related to optimizing the performance of AI models or decision-making processes, such a methodology could improve learning efficiency, adaptability, and effectiveness in AI applications ranging from autonomous systems to personalized AI services. Both these technologies could push the boundaries of AI research, fostering innovations that make AI systems more capable, resource-efficient, and tailored to complex tasks and environments. reflection: missing: The answer does not specify any concrete details about DeepSeek MoE and GRPO owing to their apparent non-existence within the currently available knowledge. It lacks specific information about their origins, creators, technical mechanisms, or documented use cases. superfluous: The theoretical discussion about Mixture of Experts (MoE) models and the speculation about 'GRPO' might be considered unnecessary in light of the question's presumed focus on actual, identifiable technologies or research findings related to 'DeepSeek MoE' and 'GRPO'. search_queries: - DeepSeek MoE technology - GRPO methodology in AI - Latest AI research breakthroughs 2024 ``` - Tool: `AnswerQuestion call_WqNVubAo7XCGZkRn0197rkqt` ```python - - url: https://joelotepawembo.medium.com/deepseekmoe-bridging-efficiency-and-capacity-in-large-language-models-using-deepseek-model-from-dbd4e852a637 content: "DeepSeekMoE: Bridging Efficiency and Capacity in Large Language Models using DeepSeek Model from China | by Joel Wembo | Jan, 2025 | Medium The DeepSeekMoE model, as outlined in the provided architecture, represents a significant advancement in the design of large-scale language models by integrating Mixture of Experts (MoE) with novel attention mechanisms and normalization strategies. DeepSeekMoE is a novel language model architecture that integrates Mixture of Experts (MoE) with a Multi-Head Latent Attention (MLA) mechanism and RMSNorm to achieve unprecedented scalability and inference efficiency. By introducing shared experts, dynamic routing, and latent variable caching, DeepSeekMoE reduces computational costs by up to 40% compared to traditional MoE models while maintaining state-of-the-art accuracy." - url: https://medium.com/yugen-ai-technology-blog/deepseek-v3-advances-in-moe-load-balancing-and-multi-token-prediction-training-f6d68c59749c content: DeepSeek-V3 — Advances in MoE Load Balancing and Multi-Token Prediction Training | by Yugen.ai | Yugen.ai Technology Blog | Jan, 2025 | Medium DeepSeek-V3 — Advances in MoE Load Balancing and Multi-Token Prediction Training 2024 has been a great year for Open Source LLMs. Towards the end of 2024, DeepSeek released DeepSeek-V3, a 671B parameter MOE (mixture-of-experts) language model. The MOE (Mixture-of-Experts) architecture uses multiple expert networks for prediction. While MoEs have gained popularity in language model architectures in the last few years, its foundations can be traced back to the Learning Factored Representations in a Deep Mixture of Experts paper. The DeepSeek-V3 paper builds on the MTP work proposed in the Better & Faster Large Language Models via Multi-token Prediction paper. - url: https://daily.dev/blog/deepseek-everything-you-need-to-know-about-this-new-llm-in-one-place content: "[DeepSeek](https://www.deepseek.com/) LLM ExplainedKey Features of DeepSeekMixture-of-Experts (MoE) SystemMulti-Head Latent Attention (MLA)Handling Long ContextsComparing DeepSeek to Other LLMsPerformance MetricsOpen-Source FrameworkUsing DeepSeek in Different FieldsFor Software DevelopmentFor Business OperationsFor EducationHow to Start Using DeepSeekSteps to Begin with DeepSeekTips for IntegrationConclusion: DeepSeek's Role in AISummary of Features and UsesFuture PossibilitiesRelated Blog Posts Explore a groundbreaking AI model that combines efficiency, top performance, and open-source accessibility for software development and automation. DeepSeek is a cutting-edge large language model (LLM) built to tackle software development, natural language processing, and business automation. This approach makes DeepSeek a practical option for developers who want to balance cost-efficiency with high performance. With its task-specific parameter activation and open-source framework, DeepSeek has made advanced AI tools more accessible to businesses of different sizes [2]. DeepSeek's open-source approach and efficient design are changing how AI is developed and used." - url: https://github.com/deepseek-ai/DeepSeek-MoE content: 'GitHub - deepseek-ai/DeepSeek-MoE: DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models DeepSeekMoE 16B is a Mixture-of-Experts (MoE) language model with 16.4B parameters. For research purposes, we release the model checkpoints of DeepSeekMoE 16B Base and DeepSeekMoE 16B Chat to the public, which can be deployed on a single GPU with 40GB of memory without the need for quantization. We evaluate DeepSeekMoE 16B on various benchmarks and compare it with a series of models, as shown in the following. DeepSeek 7B is a dense model trained on the same corpus as DeepSeekMoE 16B. We release the DeepSeekMoE 16B, including both base and chat models, to the public. model_name = "deepseek-ai/deepseek-moe-16b-chat" After data preparation, you can use the sample shell script to finetune the DeepSeekMoE model. The use of DeepSeekMoE models is subject to the Model License.' - url: https://arxiv.org/abs/2401.06066 content: "arXiv:2401.06066 In the era of large language models, Mixture-of-Experts (MoE) is a promising architecture for managing computational costs when scaling up model parameters. Starting from a modest scale with 2B parameters, we demonstrate that DeepSeekMoE 2B achieves comparable performance with GShard 2.9B, which has 1.5 times the expert parameters and computation. Subsequently, we scale up DeepSeekMoE to 16B parameters and show that it achieves comparable performance with LLaMA2 7B, with only about 40% of computations. Further, our preliminary efforts to scale up DeepSeekMoE to 145B parameters consistently validate its substantial advantages over the GShard architecture, and show its performance comparable with DeepSeek 67B, using only 28.5% (maybe even 18.2%) of computations. Cite as: arXiv:2401.06066 [cs.CL] (or arXiv:2401.06066v1 [cs.CL] for this version) Bibliographic and Citation Tools Bibliographic Explorer Toggle Connected Papers Toggle" - - url: https://aiengineering.academy/LLM/TheoryBehindFinetuning/GRPO/ content: "Group Relative Policy Optimization (GRPO) is a reinforcement learning algorithm designed to train large language models (LLMs) for complex tasks like solving math problems or writing code. The reward model, initially trained on a base model like DeepSeekMath-Base 7B with a learning rate of 2e-5, evaluates response quality based on accuracy and formatting, as noted in AWS | Community | Deep dive into Group Relative Policy Optimization (GRPO). Simplified Advantage Estimation: Using group means for baseline makes advantage calculation straightforward and efficient, reducing variance, as noted in The Math Behind DeepSeek: A Deep Dive into Group Relative Policy Optimization (GRPO) | by Sahin Ahmed, Data Scientist | Jan, 2025 | Medium." - url: https://www.marktechpost.com/2024/06/28/a-deep-dive-into-group-relative-policy-optimization-grpo-method-enhancing-mathematical-reasoning-in-open-language-models/ content: "A Deep Dive into Group Relative Policy Optimization (GRPO) Method: Enhancing Mathematical Reasoning in Open Language Models - MarkTechPost Home Tech News AI Paper Summary A Deep Dive into Group Relative Policy Optimization (GRPO) Method: Enhancing Mathematical... A Deep Dive into Group Relative Policy Optimization (GRPO) Method: Enhancing Mathematical Reasoning in Open Language Models Group Relative Policy Optimization (GRPO) is a novel reinforcement learning method introduced in the DeepSeekMath paper earlier this year. Group Relative Policy Optimization (GRPO) significantly advances reinforcement learning methods tailored for mathematical reasoning. PRIME: An Open-Source Solution for Online Reinforcement Learning with Process Rewards to Advance Reasoning Abilities of Language Models Beyond Imitation or Distillation" - url: https://medium.com/data-science-in-your-pocket/what-is-grpo-the-rl-algorithm-used-to-train-deepseek-12acc19798d3 content: "In Reinforcement Learning (RL), a policy is a strategy that an agent uses to decide which action to take in a given state. The value function helps improve the policy by estimating which states/actions lead to higher rewards. GRPO (Group Relative Policy Optimization) is a method used in reinforcement learning (RL) to help a model learn better by comparing different actions and making small, controlled updates using a group of observations. Trying Different Paths: The robot tries out a few different paths (actions) from its current strategy (policy). For a given state s, GRPO generates a group of N actions {a1,a2,…,aN} using the current policy πθ. Group Comparison: By comparing actions within a group, GRPO reduces the variance of policy updates and ensures more stable learning." - url: https://www.analyticsvidhya.com/blog/2025/02/llm-optimization/ content: Enter DeepSeek’s Group Relative Policy Optimization (GRPO)—a breakthrough that eliminated the need for explicit reward modeling by directly optimizing preference rankings. Learn the fundamentals of policy optimization, including PG, TRPO, and PPO.Explore DPO and GRPO for preference-based LLM training without explicit reward models. Unlike traditional policy optimization methods that operate on single preference pairs, GRPO leverages group-wise preference ranking, enabling better alignment with human feedback in large-scale LLM fine-tuning. Overall, the expected outcome of GRPO fine-tuning is a well-optimized model capable of generating high-quality responses ranked effectively based on preference learning. Why do LLMs need preference optimization techniques like DPO and GRPO?Ans. Traditional RL methods focus on maximizing numerical rewards, which do not always align with human expectations in language models. - url: https://medium.com/@sahin.samia/the-math-behind-deepseek-a-deep-dive-into-group-relative-policy-optimization-grpo-8a75007491ba content: This blog dives into the math behind Group Relative Policy Optimization (GRPO), the core reinforcement learning algorithm that drives DeepSeek’s exceptional reasoning capabilities. Group Relative Policy Optimization (GRPO) is a reinforcement learning (RL) algorithm specifically designed to enhance reasoning capabilities in Large Language Models (LLMs). Unlike traditional RL methods, which rely heavily on external evaluators (critics) to guide learning, GRPO optimizes the model by evaluating groups of responses relative to one another. The objective function in Group Relative Policy Optimization (GRPO) defines how the model learns to improve its policy, driving its ability to generate high-quality responses. The GRPO (Group Relative Policy Optimization) objective function is like a recipe for teaching a model to get better at generating answers by comparing its own responses and improving step by step. - - url: https://blog.google/technology/ai/2024-ai-extraordinary-progress-advancement/ content: Google released Gemini 2.0, a powerful AI model designed for the "agentic era," and integrated it into various products. We furthered our industry-leading research in AI safety, developing new tools and techniques and integrating these advances into our latest models. We expanded SynthID’s capabilities to watermarking AI-generated text in the Gemini app and web experience, and video in Veo. To help increase overall transparency online, not just with content created by Google gen AI tools, we also joined the Coalition for Content Provenance and Authenticity (C2PA) as a steering committee member and collaborated on a new, more secure version of the technical standard, Content Credentials. - url: https://www.topbots.com/ai-research-papers-2024/ content: "In this article, we delve into ten groundbreaking research papers that expand the frontiers of AI across diverse domains, including large language models, multimodal processing, video generation and editing, and the creation of interactive environments. Produced by leading research labs such as Meta, Google DeepMind, Stability AI, Anthropic, and Microsoft, these studies showcase innovative approaches, including scaling down powerful models for efficient on-device use, extending multimodal reasoning across millions of tokens, and achieving unmatched fidelity in video and audio synthesis. Unified Multimodal Processing: The research introduces a seamless integration of text and visual inputs (e.g., images, charts, and videos), expanding the model’s ability to perform complex multimodal reasoning and analysis without requiring task-specific finetuning." - url: https://blog.google/technology/ai/google-ai-big-scientific-breakthroughs-2024/ content: How Google AI is advancing science AI is rapidly advancing science, with breakthroughs in fields like protein structure prediction, brain mapping, and flood forecasting. In 2024, Google Research expanded this coverage to 100 countries and 700 million people worldwide — and improved the AI model so it offers the same accuracy at a seven-day lead time as the previous model had at five. About Google - url: https://www.forbes.com/sites/bernardmarr/2024/12/16/6-game-changing-ai-breakthroughs-that-defined-2024/ content: From Apple’s entry into generative AI to unprecedented achievements in robotics and art, 2024 marked ... Apple Enters The Generative AI Arena With Apple Intelligence By integrating OpenAI-powered generative language and graphics functionality across its product ecosystem, it created a typically refined, Apple-shaped gateway into the world of day-to-day AI for millions of non-techy people. Painting Created By AI Robot Sells For $1 Million The last couple of years have seen an explosion in AI art – but perhaps the most mind-blowing milestone was passed when Ai-Da became the first humanoid robot to sell a piece of artwork at auction. In order to do so, please follow the posting rules in our site's Terms of Service. - url: https://research.google/blog/google-research-2024-breakthroughs-for-impact-at-every-scale/ content: Never before were we able to advance technologies like AI, foundational ML, algorithms, and quantum computing with the pace, purpose and partnerships that we benefit from today, enabling us to drive innovation and address some of the greatest challenges and scientific questions of our times. In our research on learning from aggregate data, we introduced a novel approach for constructing aggregation datasets, and explored various algorithmic aspects of model learning from aggregated data, which achieved optimistic sample complexity rates in this setting. As computational techniques and ML models become progressively more advanced and accurate, our researchers are able to address challenges at scale, from GPS accuracy to the pressing issue of climate change. ``` - AI: `ReviseAnswer call_ABgsQQ0kFsl2Ppe1NzULRK20` ```python answer: |- DeepSeek MoE and GRPO have emerged as significant advancements in AI research, each contributing to the field in unique ways. DeepSeek MoE is a novel large language model (LLM) architecture that integrates Mixture of Experts (MoE) with Multi-Head Latent Attention (MLA) mechanisms and RMSNorm, offering unprecedented scalability and inference efficiency. This model reduces computational costs by up to 40% compared to traditional MoE models while maintaining state-of-the-art accuracy [1]. DeepSeek V3, another iteration, further advances MoE load balancing and multi-token prediction training [2], enhancing the performance of Open Source LLMs [3]. With its open-source framework, DeepSeek facilitates the development and use of advanced AI tools across various fields, including software development, business operations, and education [4]. GRPO, or Group Relative Policy Optimization, is a reinforcement learning algorithm designed for LLM training in complex tasks like solving math problems or writing code. It eliminates the need for explicit reward modeling by employing a preference-based approach, optimizing preference rankings, thus aligning better with human feedback during the fine-tuning of large-scale LLMs [5]. This method represents a significant leap in reinforcement learning, focusing on enhancing the reasoning capabilities of LLMs [6]. Both DeepSeek MoE and GRPO exemplify the continuous evolution of AI technologies, each holding the potential to transform future AI applications and research by making models more efficient, accessible, and aligned with human preferences. reflection: missing: The initial response overlooked specific details about DeepSeek MoE and GRPO, including their definitions, impacts, and applications in AI research. It lacked concrete information about how these technologies operate and how they contribute to advancements in AI. superfluous: The theoretical discussion of Mixture of Experts (MoE) models and speculative descriptions of GRPO's potential applications were superfluous, given that the actual technologies could have been described with concrete examples and applications. search_queries: - DeepSeek MoE applications - GRPO reinforcement learning - DeepSeek MoE efficiency improvements references: - https://joelotepawembo.medium.com/deepseekmoe-bridging-efficiency-and-capacity-in-large-language-models-using-deepseek-model-from-dbd4e852a637 - https://medium.com/yugen-ai-technology-blog/deepseek-v3-advances-in-moe-load-balancing-and-multi-token-prediction-training-f6d68c59749c - https://daily.dev/blog/deepseek-everything-you-need-to-know-about-this-new-llm-in-one-place - https://github.com/deepseek-ai/DeepSeek-MoE - https://aiengineering.academy/LLM/TheoryBehindFinetuning/GRPO/ - https://www.marktechpost.com/2024/06/28/a-deep-dive-into-group-relative-policy-optimization-grpo-method-enhancing-mathematical-reasoning-in-open-language-models/ ``` - Tool: `ReviseAnswer call_ABgsQQ0kFsl2Ppe1NzULRK20` ```python |- Error: 1 validation error for ReviseAnswer search_queries Field required [type=missing, input_value={'answer': 'DeepSeek MoE ...pen-language-models/']}}, input_type=dict] For further information visit https://errors.pydantic.dev/2.11/v/missing Please fix your mistakes. ``` - AI: `ReviseAnswer call_IpycE26HBae1o1m6bOtrHmcD` ```python answer: |- DeepSeek MoE and GRPO have emerged as significant advancements in AI research, each contributing to the field in unique ways. DeepSeek MoE is a novel large language model (LLM) architecture that integrates Mixture of Experts (MoE) with Multi-Head Latent Attention (MLA) mechanisms and RMSNorm, offering unprecedented scalability and inference efficiency. This model reduces computational costs by up to 40% compared to traditional MoE models while maintaining state-of-the-art accuracy [1]. DeepSeek V3, another iteration, further advances MoE load balancing and multi-token prediction training [2], enhancing the performance of Open Source LLMs [3]. With its open-source framework, DeepSeek facilitates the development and use of advanced AI tools across various fields, including software development, business operations, and education [4]. GRPO, or Group Relative Policy Optimization, is a reinforcement learning algorithm designed for LLM training in complex tasks like solving math problems or writing code. It eliminates the need for explicit reward modeling by employing a preference-based approach, optimizing preference rankings, thus aligning better with human feedback during the fine-tuning of large-scale LLMs [5]. This method represents a significant leap in reinforcement learning, focusing on enhancing the reasoning capabilities of LLMs [6]. Both DeepSeek MoE and GRPO exemplify the continuous evolution of AI technologies, each holding the potential to transform future AI applications and research by making models more efficient, accessible, and aligned with human preferences. reflection: missing: The initial response overlooked specific details about DeepSeek MoE and GRPO, including their definitions, impacts, and applications in AI research. It lacked concrete information about how these technologies operate and how they contribute to advancements in AI. superfluous: The theoretical discussion of Mixture of Experts (MoE) models and speculative descriptions of GRPO's potential applications were superfluous, given that the actual technologies could have been described with concrete examples and applications. search_queries: - DeepSeek MoE applications - GRPO reinforcement learning - DeepSeek MoE efficiency improvements references: - https://joelotepawembo.medium.com/deepseekmoe-bridging-efficiency-and-capacity-in-large-language-models-using-deepseek-model-from-dbd4e852a637 - https://medium.com/yugen-ai-technology-blog/deepseek-v3-advances-in-moe-load-balancing-and-multi-token-prediction-training-f6d68c59749c - https://daily.dev/blog/deepseek-everything-you-need-to-know-about-this-new-llm-in-one-place - https://github.com/deepseek-ai/DeepSeek-MoE - https://aiengineering.academy/LLM/TheoryBehindFinetuning/GRPO/ - https://www.marktechpost.com/2024/06/28/a-deep-dive-into-group-relative-policy-optimization-grpo-method-enhancing-mathematical-reasoning-in-open-language-models/ search_queries: - DeepSeek MoE applications - GRPO reinforcement learning - DeepSeek MoE efficiency improvements ``` - Tool: `ReviseAnswer call_IpycE26HBae1o1m6bOtrHmcD` ```python - - url: https://joelotepawembo.medium.com/deepseekmoe-bridging-efficiency-and-capacity-in-large-language-models-using-deepseek-model-from-dbd4e852a637 content: DeepSeekMoE is a novel language model architecture that integrates Mixture of Experts (MoE) with a Multi-Head Latent Attention (MLA) mechanism and RMSNorm. - url: https://medium.com/@joycebirkins/deepseek-4-official-papers-overview-deepseek-moe-mla-mtp-distillation-49a97b3b90a8 content: Deepseek V3 adopts an Auxiliary-Loss-Free Strategy for Load Balancing with dynamic routing and adaptive capacity, solving the load balancing problem in MoE, making it easier to train and fine-tune the model. Using the DeepSeek-R1 model to generate reasoning data for math, code, and logic puzzles, and training a specific domain expert model using supervised fine-tuning combined with reinforcement learning to handle the long-format content issue. This allows the reward model to inherit the knowledge and language understanding that DeepSeek-V3 learned during the supervised fine-tuning phase, thus better adapting to the needs of subsequent reinforcement learning tasks and reasonably evaluating the model’s outputs. Distillation involves using high-quality training data generated by the MoE large model (Deepseek V1), which includes CoTs, multi-step reasoning, answer distributions, etc. - url: https://daily.dev/blog/deepseek-everything-you-need-to-know-about-this-new-llm-in-one-place content: "[DeepSeek](https://www.deepseek.com/) LLM ExplainedKey Features of DeepSeekMixture-of-Experts (MoE) SystemMulti-Head Latent Attention (MLA)Handling Long ContextsComparing DeepSeek to Other LLMsPerformance MetricsOpen-Source FrameworkUsing DeepSeek in Different FieldsFor Software DevelopmentFor Business OperationsFor EducationHow to Start Using DeepSeekSteps to Begin with DeepSeekTips for IntegrationConclusion: DeepSeek's Role in AISummary of Features and UsesFuture PossibilitiesRelated Blog Posts Explore a groundbreaking AI model that combines efficiency, top performance, and open-source accessibility for software development and automation. DeepSeek is a cutting-edge large language model (LLM) built to tackle software development, natural language processing, and business automation. This approach makes DeepSeek a practical option for developers who want to balance cost-efficiency with high performance. With its task-specific parameter activation and open-source framework, DeepSeek has made advanced AI tools more accessible to businesses of different sizes [2]. DeepSeek's open-source approach and efficient design are changing how AI is developed and used." - url: https://tldv.io/blog/what-is-deepseek/ content: DeepSeek, like other AI models, collects and processes user inputs, but its data policies and geographic ties bring additional considerations. AI models often retain some interactions to refine their responses, but the extent of DeepSeek’s data storage remains unclear compared to more widely documented AI platforms like OpenAI’s ChatGPT. While DeepSeek presents itself as a competitive AI model, users should be aware of data privacy concerns. To recap it all, DeepSeek is an emerging AI model that has generated interest for its efficiency, structured reasoning, and potential lower cost of development compared to competitors like ChatGPT and Claude. tl;dv’s AI meeting agents automate recording, transcription, summarization, and integration into tools like CRMs and productivity platforms. - url: https://github.com/deepseek-ai/DeepSeek-MoE content: 'GitHub - deepseek-ai/DeepSeek-MoE: DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models DeepSeekMoE 16B is a Mixture-of-Experts (MoE) language model with 16.4B parameters. For research purposes, we release the model checkpoints of DeepSeekMoE 16B Base and DeepSeekMoE 16B Chat to the public, which can be deployed on a single GPU with 40GB of memory without the need for quantization. We evaluate DeepSeekMoE 16B on various benchmarks and compare it with a series of models, as shown in the following. DeepSeek 7B is a dense model trained on the same corpus as DeepSeekMoE 16B. We release the DeepSeekMoE 16B, including both base and chat models, to the public. model_name = "deepseek-ai/deepseek-moe-16b-chat" After data preparation, you can use the sample shell script to finetune the DeepSeekMoE model. The use of DeepSeekMoE models is subject to the Model License.' - - url: https://medium.com/data-science-in-your-pocket/what-is-grpo-the-rl-algorithm-used-to-train-deepseek-12acc19798d3 content: "In Reinforcement Learning (RL), a policy is a strategy that an agent uses to decide which action to take in a given state. The value function helps improve the policy by estimating which states/actions lead to higher rewards. GRPO (Group Relative Policy Optimization) is a method used in reinforcement learning (RL) to help a model learn better by comparing different actions and making small, controlled updates using a group of observations. Trying Different Paths: The robot tries out a few different paths (actions) from its current strategy (policy). For a given state s, GRPO generates a group of N actions {a1,a2,…,aN} using the current policy πθ. Group Comparison: By comparing actions within a group, GRPO reduces the variance of policy updates and ensures more stable learning." - url: https://medium.com/@sahin.samia/the-math-behind-deepseek-a-deep-dive-into-group-relative-policy-optimization-grpo-8a75007491ba content: This blog dives into the math behind Group Relative Policy Optimization (GRPO), the core reinforcement learning algorithm that drives DeepSeek’s exceptional reasoning capabilities. Group Relative Policy Optimization (GRPO) is a reinforcement learning (RL) algorithm specifically designed to enhance reasoning capabilities in Large Language Models (LLMs). Unlike traditional RL methods, which rely heavily on external evaluators (critics) to guide learning, GRPO optimizes the model by evaluating groups of responses relative to one another. The objective function in Group Relative Policy Optimization (GRPO) defines how the model learns to improve its policy, driving its ability to generate high-quality responses. The GRPO (Group Relative Policy Optimization) objective function is like a recipe for teaching a model to get better at generating answers by comparing its own responses and improving step by step. - url: https://www.marktechpost.com/2024/06/28/a-deep-dive-into-group-relative-policy-optimization-grpo-method-enhancing-mathematical-reasoning-in-open-language-models/ content: "A Deep Dive into Group Relative Policy Optimization (GRPO) Method: Enhancing Mathematical Reasoning in Open Language Models - MarkTechPost Home Tech News AI Paper Summary A Deep Dive into Group Relative Policy Optimization (GRPO) Method: Enhancing Mathematical... A Deep Dive into Group Relative Policy Optimization (GRPO) Method: Enhancing Mathematical Reasoning in Open Language Models Group Relative Policy Optimization (GRPO) is a novel reinforcement learning method introduced in the DeepSeekMath paper earlier this year. Group Relative Policy Optimization (GRPO) significantly advances reinforcement learning methods tailored for mathematical reasoning. PRIME: An Open-Source Solution for Online Reinforcement Learning with Process Rewards to Advance Reasoning Abilities of Language Models Beyond Imitation or Distillation" - url: https://community.aws/content/2rJrpj6m2eh591fjMcRZ3ushpB7/deep-dive-into-group-relative-policy-optimization-grpo?lang=en content: AWS | Community | Deep dive into Group Relative Policy Optimization (GRPO) However, as models grow larger and tasks become more complex, PPO's limitations—such as memory overhead and computational cost—have prompted the development of more advanced methods like Group Relative Policy Optimization (GRPO). The final stage uses reinforcement learning to optimize the model's policy (π_θ) using the Proximal Policy Optimization (PPO) algorithm. Now, PPO is a policy gradient method that optimizes a policy π_ø by maximizing a surrogate objective function (in this case, the approximate human preference/reward function). The group relative way that GRPO leverages to calculate the advantages, aligns well with the comparative nature of rewards models, as reward models are typically trained on datasets of comparisons between outputs on the same question. - url: https://ghost.oxen.ai/why-grpo-is-important-and-how-it-works/ content: "The process to go from DeepSeek’s base pre-trained language model to a reasoning model was laid out in detail in the DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning paper. The value and reward models are typically of a comparable parameter count to the LLM you are training. 🧠 GRPO VRAM Requirements For the GPU Poor | Oxen.ai Since the release of DeepSeek-R1, Group Relative Policy Optimization (GRPO) has become the talk of the town for Reinforcement Learning in Large Language Models due to its effectiveness and ease of training. In PPO, the value model’s original job is to try to estimate how good the tokens that are generated are, or how likely they are to give a high reward. You don’t even need a full reward model LLM in the loop during training." - - url: https://medium.com/@niejam/how-mixture-of-experts-moe-enhances-the-efficiency-of-deepseek-b0ae1b9a0092 content: MoE enhances the efficiency of DeepSeek by enabling sparse activation, task specialization, reduced computational costs, and improved training dynamics. - url: https://isitvritra101.medium.com/how-does-deepseeks-mixture-of-experts-architecture-improve-performance-08fcdab7e35a content: DeepSeek's mixture-of-experts (MoE) architecture enhances performance through computational efficiency, task specialization, and scalable resource allocation. - url: https://www.chipstrat.com/p/deepseek-moe-and-v2 content: "These constraints present significant challenges that impede the widespread adoption and utilization of LLMs. In order to tackle this problem, we introduce DeepSeek-V2, a strong open-source Mixture-of-Experts (MoE) language model, characterized by economical training and efficient inference through an innovative Transformer architecture. These constraints present significant challenges that impede the widespread adoption and utilization of LLMs. In order to tackle this problem, we introduce DeepSeek-V2, a strong open-source Mixture-of-Experts (MoE) language model, characterized by economical training and efficient inference through an innovative Transformer architecture. … DeepSeek-V2, a strong open-source Mixture-of-Experts (MoE) language model, characterized by economical training and efficient inference through an innovative Transformer architecture. DeepSeek V2's combination of MoE, MLA, and communication optimizations significantly improves training and inference efficiency compared to their dense 67B model:" - url: https://creativestrategies.com/deepseek-moe-v2/ content: Communication & Compute Optimizations – Implements load balancing and custom CUDA kernels to improve efficiency and reduce bottlenecks. Training - url: https://dev.to/sayed_ali_alkamel/deepseek-and-the-power-of-mixture-of-experts-moe-ham content: DeepSeek and the Power of Mixture of Experts (MoE) - DEV Community DeepSeek and the Power of Mixture of Experts (MoE) DeepSeek is causing a stir in the AI community with its open-source large language models (LLMs), and a key factor in its success is the Mixture of Experts (MoE) architecture. DeepSeek's innovative use of MoE has positioned it as a leading force in the world of open-source LLMs. By combining expert specialization with efficient resource utilization, DeepSeek achieves remarkable performance and scalability. DeepSeek, Mixture of Experts, MoE, Large Language Model, LLM, AI, Artificial Intelligence, Deep Learning, Natural Language Processing, NLP, Code Generation, Business Processes, Education, Open Source, Efficiency, Scalability, Performance, Training Costs, Inference Speed, DeepSeek-V3, DeepSeekMoE, Multi-Token Prediction, MTP ``` - AI: `ReviseAnswer call_w0FrbHY4x2FmYP0Xxktg6lk3` ```python answer: |- DeepSeek MoE (Mixture of Experts) and GRPO (Group Relative Policy Optimization) are groundbreaking advancements in AI research with profound implications for the future of AI technologies. **DeepSeek MoE** is a novel language model architecture integrating MoE with a Multi-Head Latent Attention (MLA) mechanism and RMSNorm, aimed at improving scalability and inference efficiency. This architecture leads to a 40% reduction in computational costs while maintaining top-notch accuracy. DeepSeek MoE, released as DeepSeek V3, further enhances MoE load balancing and multi-token prediction training, marking a leap in the development of open-source large language models (LLMs). Its open-source framework promotes the deployment of advanced AI tools in software development, business operations, and education, providing a cost-efficient yet high-performance solution [1][2][3]. **GRPO**, on the other hand, is a reinforcement learning algorithm specifically designed for training LLMs in complex tasks like solving math problems or writing code. It introduces a novel approach by eliminating the need for explicit reward modeling, instead using a preference-based approach to optimize the model. This results in improved alignment with human feedback and significantly advances reinforcement learning methods tailored for mathematical reasoning and beyond [4][5][6]. Together, DeepSeek MoE and GRPO signify a step towards making AI models more efficient, accessible, and capable of generating high-quality responses, supporting a wide range of applications across various fields. references: - https://joelotepawembo.medium.com/deepseekmoe-bridging-efficiency-and-capacity-in-large-language-models-using-deepseek-model-from-dbd4e852a637 - https://medium.com/@joycebirkins/deepseek-4-official-papers-overview-deepseek-moe-mla-mtp-distillation-49a97b3b90a8 - https://daily.dev/blog/deepseek-everything-you-need-to-know-about-this-new-llm-in-one-place - https://medium.com/data-science-in-your-pocket/what-is-grpo-the-rl-algorithm-used-to-train-deepseek-12acc19798d3 - https://medium.com/@sahin.samia/the-math-behind-deepseek-a-deep-dive-into-group-relative-policy-optimization-grpo-8a75007491ba - https://www.marktechpost.com/2024/06/28/a-deep-dive-into-group-relative-policy-optimization-grpo-method-enhancing-mathematical-reasoning-in-open-language-models/ ``` ## 3. LLM Technology Stack * **LangChain:** Used for building LLM applications, including prompt templating, output parsing, and tool usage. * **LangGraph:** Used to create stateful, multi-actor applications by defining a graph of nodes and edges. * **OpenAI GPT-4 Turbo Preview:** The LLM used for generating and refining answers. * **Tavily Search API:** Used for web search to gather additional information. * **Pydantic:** Used for data validation and schema definition. * **Dotenv:** Used for loading environment variables. ## 4. Challenges and Difficulties * **Node Duplication:** Managing the state and flow of the LangGraph required careful attention to node naming and connections. * **Dependency Management:** Ensuring all required libraries are installed and compatible. * **Import Errors:** Resolving import errors related to `langgraph` and `pydantic` versions. * **LLM Output Parsing:** Reliably parsing structured data from LLM outputs. * **Tool Integration:** Properly integrating external tools like the Tavily Search API. * **Deprecation Warnings:** Keeping up to date with LangChain updates and fixing deprecation warnings. ## 5. Future Business Impact and Further Improvements * **Enhanced Research Capabilities:** Automating research processes for various domains. * **Improved Content Generation:** Producing high-quality, well-researched content. * **Personalized Learning:** Creating adaptive learning systems that refine explanations based on user feedback. * **Automated Report Generation:** Generating detailed reports with accurate citations. * **Future Improvements:** * Implement more sophisticated critique mechanisms. * Integrate additional data sources and tools. * Add user feedback loops for continuous improvement. * Improve error handling and robustness. * Implement more complex graph structures. ## 6. Target Audience and Benefits * **Researchers:** Automate literature reviews and information gathering. * **Content Creators:** Generate high-quality, well-researched articles and reports. * **Students:** Enhance learning through iterative refinement of answers. * **Developers:** Build intelligent applications with automated research capabilities. **Benefits:** * Increased efficiency in research and content generation. * Improved accuracy and reliability of information. * Automated refinement of answers based on feedback and new information. ## 7. Advantages, Disadvantages and Tradeoffs **Advantages:** * Automated research and refinement process. * Integration of web search for up-to-date information. * Use of LangGraph for managing complex workflows. * Improved quality of generated content through iterative feedback. **Disadvantages:** * Dependency on external APIs (OpenAI, Tavily). * Potential for errors in LLM output parsing. * Complexity of managing LangGraph state and nodes. * Requires careful prompt engineering for optimal performance. **Tradeoffs:** * **Speed vs. Accuracy:** Iterative refinement takes time but improves accuracy. * **Cost vs. Performance:** Using powerful LLMs and search APIs can be costly. * **Complexity vs. Flexibility:** LangGraph provides flexibility but adds complexity. * **Automation vs. Control:** Automating research reduces manual effort but may require less manual oversight. ## 8. Setup Prerequisites * Python 3.10+ * Poetry (recommended) or pip * OpenAI API key * Tavily Search API key * LangGraph 0.3.1 1. **Clone the repository:** ```bash git clone https://github.com/junfanz1/LangGraph-Reflection-Agent.git cd LangGraph-Reflection-Agent ``` 2. **Create a virtual environment:** ```bash python3.10 -m venv venv source venv/bin/activate # On macOS/Linux venv\Scripts\activate # On Windows ``` 3. **Install dependencies:** ```bash pip install -r requirements.txt poetry add python-dotenv black isort langchain langchain-openai langgraph ``` 4. **Set environment variables:** * Create a `.env` file in the project root. * Add your OpenAI and Tavily API keys: ``` OPENAI_API_KEY=your_openai_api_key TAVILY_API_KEY=your_tavily_api_key LANGCHAIN_API_KEY=your_langchain_api_key ``` 5. **Run the main script:** ```bash python main.py ``` ## 9. Code Explanation ### `main.py` * **Purpose:** Orchestrates the LangGraph workflow. * **Functionality:** * Loads environment variables. * Defines the LangGraph using `MessageGraph`. * Adds nodes for the first responder, tool executor, and revisor. * Connects nodes with edges. * Defines a conditional edge for the event loop. * Sets the entry point and compiles the graph. * Invokes the graph with a user query. * **Crucial Functionality:** * `MessageGraph` is used to define the flow of the application. The add\_node and add\_edge functions are vital for connecting the LLM chains. The event\_loop function is used to decide the next step in the graph. * **Future Improvements:** * Implement more complex graph structures. * Add error handling and logging. ### `chains.py` * **Purpose:** Defines the LLM chains and prompts. * **Functionality:** * Loads environment variables. * Initializes the OpenAI LLM and output parsers. * Defines prompt templates for the first responder and revisor. * Creates LLM chains using `ChatPromptTemplate` and `llm.bind_tools()`. * **Crucial Functionality:** * `ChatPromptTemplate` is used to create prompts that are fed to the LLM. The `llm.bind_tools` function is used to specify the Pydantic schemas that the LLM should use to generate its responses. * **Future Improvements:** * Experiment with different prompt templates and LLM models. * Add more sophisticated output parsing. ### `tool_executor.py` * **Purpose:** Integrates Tavily web search capabilities into a LangGraph `ToolNode` for use in a reflection agent. * **Functionality:** * Initializes the Tavily Search API using `TavilySearchAPIWrapper` and `TavilySearchResults`. * Defines Pydantic models (`Reflection`, `AnswerQuestion`, `ReviseAnswer`) for structured data handling. * Creates a `run_queries` function to execute Tavily search queries in batches using `tavily_tool.batch()`. * Constructs a LangGraph `ToolNode` using `StructuredTool.from_function()` to convert the `run_queries` function into LangChain tools, specifically for `AnswerQuestion` and `ReviseAnswer` models. * **Crucial Functionality:** * The `run_queries` function facilitates efficient, batched execution of web search queries, which is essential for the agent's ability to gather information. * The `ToolNode` integration allows the agent to seamlessly incorporate web search into its LangGraph workflow, enabling dynamic information retrieval and answer refinement. * The use of `StructuredTool.from_function` correctly wraps the function into a tool, and the `name` parameter correctly names the tool, so that it can be called by the LLM. * **Future Improvements:** * Implement error handling for API requests and potential search failures to improve robustness. * Enhance result processing with features like summarization, relevance filtering, and source attribution to improve the quality of information. * Allow for configuration of search parameters such as `max_results` to provide greater flexibility. ### `schemas.py` * **Purpose:** Defines Pydantic schemas for data validation. * **Functionality:** * Defines `Reflection`, `AnswerQuestion`, and `ReviseAnswer` classes. * **Crucial Functionality:** * Pydantic schemas are used to define the structure of the data that is passed between the LLM chains. This ensures that the data is in the correct format. * **Future Improvements:** * Add more detailed validation rules. * Define schemas for other data types. ## 10. How it Works 1. The user provides a query: inputs a natural language question or request into the system (e.g., "Write about DeepSeek MoE and GRPO, list its impact and applications to future AI research."). This query is the starting point for the agent's research and refinement process. 2. Initialization and Graph Execution: The `main.py` script initializes the LangGraph, which begins at the "first_responder" node. 3. The "first_responder" node uses the `first_responder` chain from `chains.py` to generate an initial answer using the OpenAI LLM. This answer is formatted according to the `AnswerQuestion` schema. 4. The output from the "first_responder" node is passed to the "execute_tools" node. 5. The "execute_tools" node uses the `tool_node` from `tool_executor.py` to: * Extract search queries from the LLM's output, which are formatted according to the `AnswerQuestion` or `ReviseAnswer` schema. * Execute these queries in parallel using the Tavily Search API, via the `run_queries` function. * The `tool_node` encapsulates the `run_queries` function, allowing it to be used within the LangGraph workflow. 6. The search results are passed to the "revise" node. 7. The "revise" node uses the `revisor` chain from `chains.py` to refine the initial answer based on the search results and generate a critique. The refined answer is formatted according to the `ReviseAnswer` schema. 8. The `event_loop` function in `main.py` determines the next step: * If the maximum number of iterations has been reached, the process ends. * Otherwise, the process returns to the "execute_tools" node for another iteration. 9. The process repeats steps 5-8 until the maximum number of iterations is reached. 10. The final refined answer is returned as the output. ### Class/Function Breakdown * **`main.py`:** * `MessageGraph`: Defines the stateful graph. * `add_node()`: Adds nodes to the graph. * `add_edge()`: Connects nodes with edges. * `event_loop()`: Determines the next node based on the state. * `compile()`: Compiles the graph. * `invoke()`: Executes the graph. * **`chains.py`:** * `ChatOpenAI`: Initializes the OpenAI LLM. * `JsonOutputToolsParser`, `PydanticToolsParser`: Parses LLM outputs. * `ChatPromptTemplate`: Creates prompt templates. * `first_responder`: Generates the initial answer. * `revisor`: Refines the answer. * **`tool_executor.py`:** * `TavilySearchAPIWrapper`, `TavilySearchResults`: Interfaces with the Tavily Search API. * `StructuredTool`: Converts functions into tools. * `ToolNode`: Integrates tools into LangGraph. * `run_queries()`: Executes search queries in batches. * Defines Pydantic data schemas: `Reflection`, `AnswerQuestion`, `ReviseAnswer`. * **`schemas.py`:** * `Reflection`, `AnswerQuestion`, `ReviseAnswer`: Defines Pydantic data schemas. ## 11. Crucial Functions The `tool_node` integration and `run_queries()` function in `tool_executor.py` are crucial for integrating external information into the LLM's responses. Here's a detailed elaboration: * **Purpose:** * Provide a LangGraph-compatible interface to execute web search queries. * Batch and execute search queries efficiently using the Tavily Search API. * Integrate search functionality into the LangGraph workflow. * **Functionality:** 1. **Initialize Tavily Search:** * `TavilySearchAPIWrapper` and `TavilySearchResults` are used to set up the Tavily search tool. 2. **Define Pydantic Models:** * `Reflection`, `AnswerQuestion`, and `ReviseAnswer` are defined to structure data. 3. **`run_queries()` Function:** * Takes a list of `search_queries` as input. * Uses `tavily_tool.batch()` to execute the queries in parallel. * Returns the search results. 4. **`ToolNode` Integration:** * Creates a `ToolNode` instance. * Uses `StructuredTool.from_function()` to convert `run_queries` into LangChain tools. * Integrates the tools specifically for `AnswerQuestion` and `ReviseAnswer` models. * **Importance:** * The `ToolNode` allows the LangGraph agent to dynamically access and utilize web search capabilities within its workflow. * The `run_queries()` function ensures efficient, parallel execution of search queries, improving the agent's performance. * By wrapping the function in a tool, we allow the LLM to call the tavily search api, when the LLM deems it necessary. * The Pydantic models ensure that the data is structured and validated, maintaining consistency and reliability. ## 12. Future Improvements Considerations * **API Key Security:** Ensure that API keys are stored securely and not exposed in version control. * **Cost Management:** Monitor API usage and implement cost control measures. * **Prompt Engineering:** Experiment with different prompt templates to optimize the agent's performance. * **Scalability:** Consider the scalability of the application when deploying it in a production environment. * **Testing:** Implement unit and integration tests to ensure the agent's reliability. * **Documentation:** Maintain clear and up-to-date documentation for the project. * **Virtual Environments:** Always use virtual environments to manage project dependencies. * **Code Quality:** Adhere to coding standards and best practices. * **Version Control:** Use version control (e.g., Git) to track changes and collaborate with others. * **Security:** Implement security best practices to protect the application and user data. Future Improvements * **Improved Error Handling:** Implement robust error handling for API requests and LLM output parsing. * **Dynamic Tool Selection:** Allow the agent to dynamically choose tools based on the query and context. * **Knowledge Graph Integration:** Integrate knowledge graphs for more structured information retrieval. * **User Feedback Loops:** Implement mechanisms for users to provide feedback and refine the agent's behavior. * **Multi-Modal Input/Output:** Extend the agent to handle images, audio, and other data types. * **Advanced Critique Mechanisms:** Implement more sophisticated critique and reflection techniques. * **Caching:** Implement caching for API responses to reduce costs and improve performance. * **Logging and Monitoring:** Add logging and monitoring to track the agent's performance and identify potential issues. ## 13. Summary This project demonstrates the power of LangGraph and LLMs for building iterative research agents. It showcases how to combine LLM-generated content with external tools and automated feedback loops to produce high-quality, refined answers. LangGraph Components - Nodes (Python functions) - Edges (connect nodes) - Conditional Edges (make dynamic decisions to go to node A or B) State Management: dictionary to track the graph’s execution result, chat history, etc. Reflection Agents: prompt to improve quality and success rate of agents/AI systems. ### LangGraph Researcher Agent https://github.com/assafelovic/gpt-researcher - Implementing agent production-ready. There’re nodes and edges, but no cycles. We can integrate GPT Researcher (as a node under LangGraph graph) within Multi-Agent Architecture. (https://github.com/assafelovic/gpt-researcher/tree/master/multi_agents) - Every agent in a multi-agent system can be a researcher, as part of workflow. e.g., `Technology` agent is talor-made for technological subjects, and is dynamically created/chosen - Research automation needs to make a decision for a few deeper levels and iterate again again again until the optimal answer. Key difference here is not only width (in parallel then aggregation) but also depth ### Reason for LangGraph in Multi-Agent Architecture - LangGraph (Flow Engineering techniques addresses the tradeoff between agent freedom and our control) is more flexible in production than CrewAI (doesn’t have as much control of the flow) - Breaks down the problem into specific actions, like microservices, (1) with specialized tasks, we can control quality of nodes, (2) can scale up the application as it grows - Customizability, creative framework - Contextual compression is the best method for retrieving in RAG workflow - Allow both web and local data indexing, with LangChain easy integration can embed anything - Human-in-the-loop, let user decide how much feedback autonomy to interact with, especially useful when finding two knowledge sources that conflict or contradict each other. When this happens, AI needs human assistance. ### GPT Researcher Example

![image](https://github.com/user-attachments/assets/0550713b-0291-4e91-836c-3f3cdf7305db) GPT Research Report: https://github.com/junfanz1/LangGraph-Reflection-Agent/blob/main/gpt-researcher.ipynb Input: ```python query = "Why DeepSeek's MoE and GRPO is a successful architecture in LLM research and application?" report_type = "research_report" ``` Output: https://github.com/junfanz1/LangGraph-Reflection-Researcher/blob/main/Why%20DeepSeek's%20MoE%20and%20GRPO%20is%20a%20Successful%20Architecture%20in%20LLM%20Research%20and%20Application.md --- ## 14. Debugging Notes https://github.com/langchain-ai/langgraph/issues/3637#issuecomment-2690150631 https://github.com/langchain-ai/langgraph/releases

```python ############ # tool_executor.py Update # ToolInvocation, ToolExecutor have been deprecated after LangGraph 3.0, it is recommended to replace them with ToolNode ############ ``` ## Acknowledgements [Eden Marco: LangGraph-Develop LLM powered AI agents with LangGraph](https://www.udemy.com/course/langgraph)